Persistent and timely monitoring of agricultural farmlands have shown to be increasingly valuable to crop health and resource management. Remote sensing satellites and airborne sensing with winged aircrafts have allowed scientists to map large farmlands and forests through acquisition of multi-spectral imagery and 3-D structural data. However, data from these platforms lack the spatio-temporal resolution necessary for precision agriculture. For example, a typical remote sensing satellite image may have a pixel resolution of hundreds of meters, and airborne sensing may provide resolution of a few meters. It is desirable, however, to obtain data for monitoring orchard or vineyard health at a centimeter scale—a resolution at which stems, leaves, and fruits can be observed.
As a result, farm management tasks such as yield estimation and disease monitoring are primarily carried out through visual inspection by human scouts. Recent development in this area has resulted in imaging systems and data analysis methodologies to help automate some of these tasks (See, e.g., U.S. Patent Application Pub. No. 2013/0325346). For example, unmanned ground vehicles (UGVs) have been the first step towards automating the close-range monitoring of high-value crops. They can carry a variety of bulky sensors such as LiDAR for volumetric mapping, and ground penetrating radar (GPR) and electrical conductance sensors for precise soil mapping. Due to the mobility constraints of unstructured farms, however, it is infeasible to use UGVs for rapid and persistent monitoring. Additionally, ground vehicles are intrusive. Aerial platforms and hand-held sensors can alleviate some of the problems with using UGVs, but the available platforms for such systems are bulky and expensive, which can be prohibitive for large-scale deployments in farms. Furthermore, the spatio-temporal resolution of such systems are considered inadequate as discussed above.
Accordingly, it would be desirable to develop a portable, low-cost, compact, and lightweight imaging system along with agile deployment methodologies to help growers observe farms efficiently. As a part of this system, it can be advantageous to have a powerful data analysis and visualization framework to help growers interpret the acquired data.